1. Field of the Invention
The present invention relates to image search, particularly to an object-based image search.
2. Description of the Related Art
Due to the fast advance of computer science, digital contents, particularly digital image data, expand explosively. Thus, how to obtain desired images from massive image data becomes an important topic.
One of the existing search methods is using contents-description words to search for desired images. However, annotating a massive image data is a laborious work. Further, the semantic gap between the annotator and the searcher often influences the search results.
A U.S. Pat. No. 6,859,802 B1 disclosed a method using “user's relevance feedback” to search for desired images. However, the “user's relevant feedback” usually cannot indeed express the target images. For example, when a user intends to search for images having a beach, he selects several pictures containing a beach and feedbacks them to a search system. The system analyzes the components of those pictures, such as colors, textures, edges, etc., and finally recognizes that all those pictures contain a beach. The common feature is then used as the key feature to find out beach-containing pictures from the database. However, a picture containing a beach usually also contains a scene of sea. Thus, the search system is likely to regard “sea” as the common feature and thus retrieves incorrect pictures containing only “sea” but without a beach. To overcome such a problem, the user has to provide the system sample pictures containing “beach” but without “sea”. However, it is a laborious and troublesome task. Therefore, complicated algorithms were proposed to improve the abovementioned drawback. Nevertheless, target objects are still hard to directly and correctly define. For the details, refer to Xiang Sean Zhou and T. S. Huang, “Relevance Feedback for Image Retrieval: a Comprehensive Review”, ACM Multimedia Systems, 8(6): 536-544, 2003.
Recently, some researchers have proposed object-based image search system, wherein a user defines key objects in sample images, and the system performs searches according the key objects (Refer to M. S. Drew, Z. N. Li, and Z. Tauber, “Illumination Color Covariant Locale-Based Visual Object Retrieval”, Pattern Recognition, 35(8): 87-1704, 2002). Such a method enables users to directly define target objects and thus has much better search results than previous methods. However, defining target objects in sample images usually needs an image-segmenting software tool, such as a “Magic Wand” or a “Snake” (Refer to Ze-Nian Li and Mark S. Drew, “Fundamentals of Multimedia-Chapter 18”, Pearson Prentice Hall PTR, Upper Saddle River, N.J., 2004). Thus, users have to install and learn these tools. It is indeed a trouble for those who just intend to search for desired pictures but do not want to learn any image-segmenting software tool. The biggest drawback of this method is that the image-segmenting tool cannot precisely segment an object from the image, which has been a stickler in the field for many years. When searches are based on the features extracted from the inaccurate objects segmented by the abovementioned tool, the search results are unlikely to be satisfactory naturally.
Accordingly, the present invention proposes an object-based image search system and a method thereof to overcome the abovementioned problems.